The invention relates to a method for generating a high-resolution 3D voxel data set of an object to be examined, with the aid of a computed tomography scanner, and to a computer program product in this regard.
Computed tomography (CT) is a method for obtaining information about the interior of objects. Computed tomography originates from the medical field, but in the meantime is also used in the industrial field for material analysis and for non-destructive examinations. The resolution of a CT recording depends on the distance between X-ray source and object. The smaller this distance, the higher the geometric magnification and thus also the resolution of the recording. The achievable resolution is limited in particular by the fact that the object has to be rotated by 180 degrees during the recording and, therefore, the distance between X-ray source and object cannot be arbitrarily small.
In other words, the resolution of computed tomography recordings, in particular of flat or planar structures (e.g. printed circuit boards), is limited by the fact that it is necessary to rotate the object by at least 180 degrees.
By means of two-dimensional (2D) recordings, i.e. recordings for which the object is not rotated, significantly higher resolutions can be achieved since the object can be brought significantly closer to the X-ray source. However, 2D recordings have the disadvantage that, in the case of objects consisting of a plurality of planes, these planes are all superimposed in the recording. Moreover, 2D X-ray recordings show only part of the object.
Therefore, a problem addressed by the present invention is that of providing a method by which the resolution of three-dimensional (3D) images in computed tomography can be increased.
This problem is solved by the subjects of the alternative independent claims. The dependent claims relate to advantageous embodiments.
A first independent aspect for solving the problem relates to a method for generating a high-resolution 3D voxel data set of an object to be examined, with the aid of a computed tomography scanner, comprising the following steps:                Generating a 3D image data set by acquiring a multiplicity of computed tomography images of the object;        Generating a 2D image data set by acquiring one or a plurality of additional images of the object, wherein the additional images for the 2D image data set have a higher resolution in comparison with the images acquired for the 3D image data set;        Generating the 3D voxel data set of the object to be examined, with the aid of an image data reconstruction algorithm that uses both the generated 3D image data set and the generated 2D image data set as input data set.        
Within the meaning of this description a voxel data set or else volume data set is understood to be a data set comprising a multiplicity of voxels. In this case, a voxel is a grid point or image point in a three-dimensional grid or coordinate system. The multiplicity of voxels of the voxel data set thus constitute the three-dimensional volume of the object to be examined in the form of discrete points. The voxel data set comprises for each voxel a value that describes the attenuation of X-ray radiation at the location of the voxel, i.e. at a specific three-dimensional point of the object to be examined.
The term “high-resolution” is intended merely to mean that the resolution of the 3D voxel data set generated by the method according to the invention is higher than in conventional methods.
The term “acquiring” images encompasses, in particular, recording or measuring images.
A 3D image data set is generated by acquiring a multiplicity of computed tomography images, i.e. a series of images or an image sequence of the object. The images are acquired or measured with the aid of one or more detectors, in particular by means of a flat panel image detector, of the computed tomography scanner. In particular, the 3D image data set comprises a multiplicity of computed tomography images or a series of images or an image sequence. The individual images of the 3D image data set are acquired in each case from different perspectives or at different acquisition or recording angles. In particular, each image can be assigned to a specific perspective or to a specific recording angle. The recording angle is e.g. the angle between an axis, e.g. the longitudinal axis, of the object to be examined and an optical axis of the computed tomography scanner. The optical axis can be defined e.g. by the connecting line between X-ray source and detector of the computed tomography scanner. Preferably, the 3D image data set comprises images for recording angles of 0° to 180°, even more preferably of 0° to 360°.
The images acquired for the 3D image data set are so-called standard CT images, i.e. images having a standard resolution. Within the meaning of this description, standard images are understood to be in particular such images which are recorded if the object is arranged in a so-called standard position. In the standard position, the object is arranged between the X-ray source and the at least one detector in such a way that it is rotatable, in particular rotatable by 180° or preferably by 360°. In the standard position, the object to be examined thus cannot be situated arbitrarily near to the X-ray source, for which reason the resolution of such standard images is limited to a standard resolution. In other words, the standard resolution is that resolution with which CT images are conventionally acquired for a 3D measurement.
In addition to the computed tomography images acquired for the 3D image data set, according to the invention one or a plurality of additional images are also acquired as well, which serve for generating a 2D image data set. Said additional images have a higher resolution in comparison with the standard images, i.e. in comparison with the images acquired for the 3D image data set. The higher resolution can be achieved e.g. by virtue of the fact that the object to be examined is arranged nearer to the X-ray source in comparison with the standard position. In this way, the geometric magnification and, consequently, also the resolution of the acquired images or image data can be increased. When acquiring the additional images for the 2D image data set, the object need not be rotated, for which reason it can be arranged arbitrarily near to the X-ray source.
The at least one additional image can be acquired from one or a plurality of selected regions of the object. Preferably, a multiplicity of additional images are acquired, i.e. a series of additional images or an additional image sequence. The different additional images acquired can in this case image respectively different regions of the object.
Preferably, the 3D image data set and/or the 2D image data set furthermore comprise(s) for each acquired image metadata describing the position and/or the perspective or the recording angle of the object.
Both the 3D image data set, i.e. the image data set generated from the standard images, and the 2D image data set, i.e. the image data set generated from the high- or higher-resolution images, form the input data set for an, in particular iterative, image data reconstruction algorithm used to generate or calculate the high-resolution 3D voxel data set of the object to be examined.
For the image data reconstruction algorithm the resolution of the result volume or of the voxel data set is preferably chosen such that a detector pixel divided by a magnification factor of the 2D image data set generation corresponds to a voxel of the result volume, wherein the magnification factor corresponds to the focus-detector distance divided by the focus-object distance during the 2D image data set generation.
The method according to the invention thus advantageously combines the data from a normal or standard 3D computed tomography data set with a high-resolution 2D data set in order to create a high-resolution 3D data set therefrom. In other words, the low-resolution 3D data and the high-resolution 2D data are advantageously combined in order to obtain a 3D data set in which the individual planes are separated, but in which the high resolution of the 2D recordings parallel to the planes is maintained.
In the method according to the invention, therefore, the X-ray recordings effected for the normal CT and the high-resolution 2D X-ray recordings are reconstructed simultaneously to form a high-resolution voxel data set with the aid of the image data reconstruction algorithm.
In one preferred embodiment of the method according to the invention, the object for generating the 3D image data set is arranged in a first position, preferably rotatably, between an X-ray source and a detector of the computed tomography scanner. For generating the 2D image data set the object is arranged in a second position, whose distance from the X-ray source is smaller in comparison with the first position.
The first position corresponds to the standard position already mentioned further above. In said standard position, a normal CT recording, i.e. a CT recording with a conventional resolution, can thus be effected. The focus-object distance or the distance between the object and the X-ray source in this first position is large enough that the object can rotate and the entire object can be imaged onto the detector during the rotation.
The second position can correspondingly also be designated as high-resolution position. High-resolution 2D X-ray recordings with a smaller focus-object distance are effected in this second position. Preferably, the object is arranged with a longitudinal axis orthogonal to the optical axis or to the connecting line between focus or X-ray source and midpoint of the detector. Since the object need not be rotated for generating the 2D image data set, the focus-object distance can be chosen to be significantly smaller and thus the magnification can be chosen to be significantly greater.
The respective distances or positions of the object are dependent, in principle on the shape and size of the object. By way of example, the distance between the object and the X-ray source in the first position or standard position may be approximately 5 cm while the distance between the object and the X-ray source in the second position or high-resolution position may be only approximately 1 cm. It goes without saying that these values represent only exemplary values.
In a further preferred embodiment of the method according to the invention, generating the 3D image data set of the object comprises rotating the object, in particular by 360°. Alternatively or additionally, generating the 2D image data set of the object comprises displacing the object in a plane arranged perpendicular to the longitudinal axis or optical axis of the computed tomography scanner.
On account of the high magnification achieved when generating the 2D image data set, i.e. if the object is situated in the second position or in the high-resolution position, it may be that only parts of the object are visible on the detector. In order nevertheless to image larger regions, a plurality of recordings can be effected, wherein the object is displaced orthogonally with respect to the connecting line between focus or X-ray source and detector midpoint between the recordings. If the direction of the connecting line between X-ray source and detector midpoint is designated as the Z-axis, then the object is thus displaced in the X- and/or Y-direction, i.e. in the X-Y plane. In this case, the step size is preferably chosen to be small in order to minimize the effect of individual planes of the object on neighbouring planes during the reconstruction. By way of example, the step size may be one tenth of the detector size divided by the magnification factor.
In a further preferred embodiment of the method according to the invention, the image data reconstruction algorithm is based on a Maximum Likelihood Expectation Maximization (MLEM) algorithm. In particular, the image data reconstruction algorithm is a Maximum Likelihood Expectation Maximization (MLEM) algorithm.
In a further preferred embodiment of the method according to the invention, the image data reconstruction algorithm comprises calculating a normalization volume data set, wherein the normalization volume data set results as the sum of a normalization volume data set associated with the 3D image data set and a normalization volume data set associated with the 2D image data set and weighted with a weighting factor.
Expressed in formulae, the normalization volume data set norm is calculated as follows:norm=P1T(normseq1)+w·P2T(normseq2)  (1), whereinPT(I) generally represents a transposed projection or a back projection of an image sequence I. The index 1 in equation (1) means that the back projection relates to a first image data set, namely the 3D image data set having standard resolution, while the index 2 correspondingly means that the back projection relates to a second image data set, namely the 2D image data set having higher resolution. normseq1 denotes a normalized image sequence of the 3D image data set and normseq2 denotes a normalized image sequence of the 2D image data set. In equation (1), w is the weighting factor. The weighting factor w can have an arbitrary positive value, e.g. the value 5, depending on the application. In particular, it may be the case that w=1.
In particular, in equation (1)normseq1:=1 and normseq2:=1  (2)are defined.
In a further preferred embodiment of the method according to the invention, the image data reconstruction algorithm comprises calculating a projection associated with the 3D image data set and a projection associated with the 2D image data set.
Expressed in formulae, a projection associated with the 3D image data setproj1:=P1(voln)  (3)and a projection associated with the 2D image data setproj2:=P2(voln)  (4)are calculated. In this case, voln denotes the volume data set in the n-th iteration step.
In a further preferred embodiment of the method according to the invention, each pixel of the generated 3D image data set is divided by the corresponding pixel of the projection associated with the 3D image data set, as a result of which a modulated projection
      proj    1    *    :=            input      1              proj      1      associated with the 3D image data set is obtained. Furthermore, each pixel of the generated 2D image data set is divided by the corresponding pixel of the projection associated with the 2D image data set, as a result of which a modulated projection
      proj    2    *    :=            input      2              proj      2      associated with the 2D image data set is obtained.
In a further preferred embodiment of the method according to the invention, a, preferably unfiltered, back projection is calculated on the basis of the modulated projection proj1* associated with the 3D image data set and the modulated projection proj2* associated with the 2D image data set.
In a further preferred embodiment of the method according to the invention, the back projection is calculated as the sum of a, preferably unfiltered, back projection associated with the 3D image data set and a, preferably unfiltered, back projection associated with the 2D image data set and weighted with a weighting factor.
Expressed in formulae, this back projection is calculated as follows:backproj:=P1T(proj1*)+w·P2T(proj2*)  (5),wherein w is the weighting factor. In particular, the weighting factors in equation (1) and equation (5) are identical.
A further independent aspect for solving the problem relates to a computer program product comprising machine-readable program code which, when loaded on a computer, is suitable for performing the method according to the invention.
For the abovementioned further independent aspect and in particular for preferred embodiments in this regard, the explanations given above or below concerning the embodiments of the first aspect also hold true. In particular, for one independent aspect of the present invention and for preferred embodiments in this regard, the explanations given above and below concerning the embodiments of the respective other aspects also hold true.
Individual embodiments for solving the problem are described by way of example below with reference to the figures. In this case, the individual embodiments described have in part features which are not absolutely necessary for implementing the claimed subject matter, but which provide desired properties in specific applications. In this regard embodiments which do not have all the features of the embodiments described below are also intended to be regarded as disclosed in a manner coming under the technical teaching described. Furthermore, in order to avoid unnecessary repetitions, specific features are mentioned only with regard to individual embodiments from among the embodiments described below. It is pointed out that the individual embodiments are therefore intended to be considered not only by themselves but also in a joint consideration. On the basis of this joint consideration the person skilled in the art will recognize that individual embodiments can also be modified by inclusion of individual or a plurality of features of other embodiments. It is pointed out that a systematic combination of the individual embodiments with individual or a plurality of features described with regard to other embodiments may be desirable and expedient and is therefore intended to be taken into account and also to be regarded as encompassed by the description.